Characterisation of cylindrical curves

We employ moving frames along pairs of curves at constant separation to derive various conditions for a curve to belong to the surface of a circular cylinder

Model-based recognition of curves and surfaces using tactile data

Model-based object recognition has mostly been studied over inputs including images and range data. Though such data are global, cameras and range sensors are subject to occlusions and clutters, which often make recognition difficult and computationally expensive. In contrast, touch by a robot hand is free of occlusion and clutter issues, and recognition over tactile data can be more efficient.;In this thesis, we investigate model-based recognition of two and three dimensional curved objects from tactile data. The recognition of 2D objects is an invariant-based approach. We have derived differential and semi-differential invariants for quadratic curves and special cubic curves that are found in applications. These invariants, independent of translation and rotation, can be computed from local geometry of a curve. Invariants for quadratic curves are the functions in terms of the curvature and its derivative with respect to arc length. For cubic curves, the derived invariants also involve a slope in their expressions. Recognition of a curve reduces to invariant verification with its canonical parametric form determined along the way. In addition, the contact locations with the robot hand are found on the curve, thereby localizing it relative to the touch sensor. We have verified the correctness of all invariants by simulations. We have also shown that the shape parameters of the recognized curve can be recovered with small errors. The byproduct is a procedure that reliably estimates curvature and its derivative from real tactile data. The presented work distinguishes itself from traditional model-based recognition in its ability to simultaneously recognize and localize a shape from one of several classes, each consisting of a continuum of shapes, by the use of local data.;The recognition of 3D objects is based on registration and consists of two steps. First, a robotic hand with touch sensors samples data points on the object\u27s surface along three concurrent curves. The two principal curvatures at the curve intersection point are estimated and then used in a table lookup to find surface points that have similar local geometries. Next, starting at each such point, a local search is conducted to superpose the tactile data onto the surface model. Recognition of the model is based on the quality of this registration. The presented method can recognize algebraic as well as free-form surfaces, as demonstrated via simulations and robot experiments. One difference in the recognition of these two sets of shapes lies in the principal curvature estimation, which are calculated from the close forms and estimated through fitting, respectively. The other difference lies in data registration, which is carried out by nonlinear optimization and a greedy algorithm, respectively

Mechanical design for the tactile exploration of constrained internal geometries

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.MIT Institute Archives copy: with CD-ROM; divisional library copy with no CD-ROM.Includes bibliographical references (p. 93-98).Rising world oil prices and advanced oil recovery techniques have made it economically attractive to rehabilitate abandoned oil wells. This requires guiding tools through well junctions where divergent branches leave the main wellbore. The unknown locations and shapes of these junctions must be determined. Harsh down-well conditions prevent the use of ranged sensors. However, robotic tactile exploration using a manipulator is well suited to this problem. This tactile characterization must be done quickly because of the high costs of working on oil wells. Consequently, intelligent tactile exploration algorithms that can characterize a shape using sparse data sets must be developed. This thesis explores the design and system architecture of robotic manipulators for down-well tactile exploration. A design approach minimizing sensing is adopted to produce a system that is mechanically robust and suited to the harsh down-well environment. A feasibility study on down-well tactile exploration manipulators is conducted. This study focuses on the mature robotic technology of link and joint manipulators with zero or low kinematic redundancy. This study produces a field system architecture that specifies a unified combination of control, sensing, kinematic solutions for down-well applications. An experimental system is built to demonstrate the proposed field system architecture and test control and intelligent tactile exploration algorithms. Experimental results to date have indicated acceptability of the proposed field system architecture and have demonstrated the ability to characterize geometry with sparse tactile data.(cont.) Serpentine manipulators implemented using digital mechatronic actuation are also considered. Digital mechatronic devices use actuators with discrete output states and the potential to be mechanically robust and inexpensive. The design of digital mechatronic devices is challenging. Design parameter optimization methods are developed and applied to a design case study of a manipulator in a constrained workspace. This research demonstrates that down-well tactile exploration with a manipulator is feasible. Experimental results show that the proposed field system architecture, a 4 degree-of-freedom anthropomorphic manipulator, can obtain accurate tactile data without using any sensor feedback besides manipulator joint angles.by Daniel Terrance Kettler.S.M

Recognizing 3D Objects Using Tactile Sensing and Curve Invariants

A general paradigm for recognizing 3D objects is offered, and applied to some geometric primitives (spheres, cylinders, cones, and tori). The assumption is that a curve on the surface, or a pair of intersecting curves, was measured with high accuracy (for instance, by a sensory robot). Differential invariants of the curve(s) are then used to recognize the surface. The motivation is twofold: the output of some devices is not surface range data, but such curves. Also, a considerable speedup is obtained by using curve data, as opposed to surface data which usually contains a much higher number of points

Recognizing 3D Objects Using Tactile Sensing and Curve Invariants

A general paradigm for recognizing 3D objects is offered, and applied to some geometric primitives (spheres, cylinders, cones, and tori). The assumption is that a curve on the surface, or a pair of intersecting curves, was measured with high accuracy (for instance, by a sensory robot). Differential invariants of the curve(s) are then used to recognize the surface. The motivation is twofold: the output of some devices is not surface range data, but such curves. Also, a considerable speedup is obtained by using curve data, as opposed to surface data which usually contains a much higher number of points. We survey global, algebraic methods for recognizing surfaces, and point out their limitations. After introducing some notions from differential geometry and elimination theory, the differential and "semi-differential" approach to the problem is described, and novel invariants which are based on the curve's curvature and torsion are derived. 1 Introduction and Previous Work One task an ..

Tactile mapping of harsh, constrained environments, with an application to oil wells

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. [110]-114).This work develops a practical approach to explore rough environments when time is critical. The harsh environmental conditions prevent the use of range, force/torque or tactile sensors. A representative case is the mapping of oil wells. In these conditions, tactile exploration is appealing. In this work, the environment is mapped tactilely, by a manipulator whose only sensors are joint encoders. The robot autonomously explores the environment collecting few, sparse tactile data and monitoring its free movements. These data are used to create a model of the surface in real time and to choose the robot's movements to reduce the mapping time. First, the approach is described and its feasibility demonstrated. Real-time impedance control allows a robust robot movement and the detection of the surface using a manipulator mounting only position sensors. A representation based on geometric primitives describes the surface using the few, sparse data available. The robustness of the method is tested against surface roughness and different surrounding fluids. Joint backlash strongly affect the robot's precision, and it is inevitable because of the thermal expansion in the joints. Here, a new strategy is developed to compensate for backlash positioning errors, by simultaneously identifying the surface and the backlash values. Second, an exploration strategy to map a constraining environment with a manipulator is developed. To maximize the use of the acquired data, this work proposes a hybrid approach involving both workspace and configuration space. The amount of knowledge of the environment is evaluated with an approach based on information theory, and the robot's movements are chosen to maximize the expected increase of such knowledge. Since the robot only possesses position sensors, the location along the robot where contact with the surface occurs cannot be determined with certainty. Thus a new approach is developed, that evaluates the probability of contact with specific parts of the robot and classifies and uses the data according to the different types of contact. This work is validated with simulations and experiments with a prototype manipulator specifically designed for this application.by Francesco Mazzini.Ph.D